SeMRA

The Semantic Mapping Reasoner and Assembler (SeMRA) is a Python package that provides:

1. An object model for semantic mappings (based on SSSOM)
2. Functionality for assembling and reasoning over semantic mappings at scale
3. A provenance model for automatically generated mappings
4. A confidence model granular at the curator-level, mapping set-level, and community feedback-level

We also provide the SeMRA Raw Semantic Mappings Database, a set of pre-assembled semantic mappings from hundreds of ontologies and databases, on Zenodo at https://doi.org/10.5281/zenodo.11082038 that can be rebuilt with semra build. More information here.

💪 Getting Started

Here's a demonstration of SeMRA's object, provenance, and cascading confidence model:

```python from semra import Reference, Mapping, EXACTMATCH, SimpleEvidence, MappingSet, MANUALMAPPING

r1 = Reference(prefix="chebi", identifier="107635", name="2,3-diacetyloxybenzoic") r2 = Reference(prefix="mesh", identifier="C011748", name="tosiben")

mapping = Mapping( subject=r1, predicate=EXACTMATCH, object=r2, evidence=[ SimpleEvidence( justification=MANUALMAPPING, confidence=0.99, author=Reference(prefix="orcid", identifier="0000-0003-4423-4370", name="Charles Tapley Hoyt"), mapping_set=MappingSet( name="biomappings", license="CC0", confidence=0.90, ), ) ] ) ```

Assembly

Mappings can be assembled from many source formats using I/O functions exposed through the top-level semra submodule:

```python import semra

load mappings from any standardized SSSOM file as a file path or URL, via pandas.read_csv

sssomurl = "https://w3id.org/biopragmatics/biomappings/sssom/biomappings.sssom.tsv" mappings = semra.fromsssom( sssomurl, license="spdx:CC0-1.0", mappingset_title="biomappings", )

alternatively, metadata can be passed via a file/URL

mappingsalt = semra.fromsssom( sssom_url, metadata="https://w3id.org/biopragmatics/biomappings/sssom/biomappings.sssom.yml" )

load mappings from the Gene Ontology (via OBO format)

gomappings = semra.frompyobo("go")

load mappings from the Uber Anatomy Ontology (via OWL format)

uberonmappings = semra.frombioontologies("uberon") ```

SeMRA also implements custom importers in the semra.sources submodule. It's based on a pluggable architecture (via class-resovler) so additional custom sources can be incorporated without modifying the SeMRA source code.

```python from semra.sources import getomimgene_mappings

omimgenemappings = getomimgene_mappings() ```

Inference

SeMRA implements the chaining and inference rules described in the SSSOM specification. The first rule is inversions:

```python from semra import Mapping, EXACTMATCH, Reference from semra.inference import inferreversible

r1 = Reference(prefix="chebi", identifier="107635", name="2,3-diacetyloxybenzoic") r2 = Reference(prefix="mesh", identifier="C011748", name="tosiben")

mapping = Mapping(subject=r1, predicate=EXACT_MATCH, object=r2)

includes the mesh -> exact match-> chebi mapping with full provenance

mappings = infer_reversible([mapping]) ```

mermaid graph LR A[2,3-diacetyloxybenzoic<br/>chebi:107635] -- skos:exactMatch --> B[tosiben<br/>mesh:C011748] B -. "skos:exactMatch<br/>(inferred)" .-> A

The second rule is about transitivity. This means some predicates apply over chains. SeMRA further implements configuration for two-length chains and could be extended to arbitrary chains.

```python from semra import Reference, Mapping, EXACTMATCH from semra.inference import inferchains

r1 = Reference.fromcurie("mesh:C406527", name="R 115866") r2 = Reference.fromcurie("chebi:101854", name="talarozole") r3 = Reference.from_curie("chembl.compound:CHEMBL459505", name="TALAROZOLE")

m1 = Mapping(subject=r1, predicate=EXACTMATCH, object=r2) m2 = Mapping(subject=r2, predicate=EXACTMATCH, object=r3)

infers r1 -> exact match -> r3

mappings = infer_chains([m1, m2]) ```

mermaid graph LR A[R 115866<br/>mesh:C406527] -- skos:exactMatch --> B[talarozole<br/>chebi:101854] B -- skos:exactMatch --> C[TALAROZOLE<br/>chembl.compound:CHEMBL459505] A -. "skos:exactMatch<br/>(inferred)" .-> C

The third rule is generalization, which means that a more strict predicate can be relaxed to a less specific predicate, like owl:equivalentTo to skos:exactMatch.

```python from semra import Reference, Mapping, EXACTMATCH from semra.inference import infergeneralizations

r1 = Reference.fromcurie("chebi:101854", name="talarozole") r2 = Reference.fromcurie("chembl.compound:CHEMBL459505", name="TALAROZOLE")

m1 = Mapping(subject=r1, predicate=EXACT_MATCH, object=r2)

mappings = infer_generalizations([m1]) ```

mermaid graph LR A[talarozole<br/>chebi:101854] -- owl:equivalentTo --> B[TALAROZOLE<br/>chembl.compound:CHEMBL459505] A -. "skos:exactMatch<br/>(inferred)" .-> B

The third rule can actually be generalized to any kinds of mutation of one predicate to another, given some domain knowledge. For example, some resources curate oboInOwl:hasDbXref predicates when it's implied that they mean skos:exactMatch because the resource is curated in the OBO flat file format.

```python from semra import Reference, Mapping, DBXREF from semra.inference import inferdbxref_mutations

r1 = Reference.fromcurie("doid:0050577", name="cranioectodermal dysplasia") r2 = Reference.fromcurie("mesh:C562966", name="Cranioectodermal Dysplasia") m1 = Mapping(subject=r1, predicate=DB_XREF, object=r2)

we're 99% confident doid-mesh dbxrefs actually are exact matches

mappings = inferdbxrefmutations([m1], {("doid", "mesh"): 0.99}) ```

mermaid graph LR A[cranioectodermal dysplasia<br/>doid:0050577] -- oboInOwl:hasDbXref --> B[Cranioectodermal Dysplasia<br/>mesh:C562966] A -. "skos:exactMatch<br/>(inferred)" .-> B

Processing

Mappings can be processed, aggregated, and summarized using functions from the semra.api submodule:

```python from semra.api import filterminimumconfidence, prioritize, project, summarize_prefixes

mappings = ... mappings = filterminimumconfidence(mappings, cutoff=0.7)

summarydf = summarizeprefixes(mappings)

get one-to-one mappings between entities from the given prefixes

chebitomesh = project(mappings, sourceprefix="chebi", targetprefix="mesh")

process the mappings using a graph algorithm that creates

a "star" graph for every equivalent entity, where the center

of the star is determined by the equivalent entity with the

highest priority based on the given list

priority_mapping = prioritize(mappings, priority=[ "chebi", "chembl.compound", "pubchem.compound", "drugbank", ]) ```

The prioritization described by the code above works like this:

mermaid graph LR subgraph unprocessed [Exact Matches Graph] A[R 115866<br/>mesh:C406527] --- B[talarozole<br/>chebi:101854] B --- C[TALAROZOLE<br/>chembl.compound:CHEMBL459505] A --- C end subgraph star [Prioritized Mapping Graph] D[R 115866<br/>mesh:C406527] --> E[talarozole<br/>chebi:101854] F[TALAROZOLE<br/>chembl.compound:CHEMBL459505] --> E end unprocessed --> star

🏞️ Landscape Analysis

We demonstrate using SeMRA to assess the landscape of five biomedical entity types:

1. Disease
2. Cell & Cell Line
3. Anatomy
4. Protein Complex
5. Gene

These analyses are based on declarative configurations for sources, processing rules, and inference rules that can be found in the semra.landscape module of the source code. These can be rebuilt with semra landscape, with more documentation here.

🤖 Tools for Data Scientists

SeMRA provides tools for data scientists to standardize references using semantic mappings.

For example, the drug indications table in ChEMBL contains a variety of references to EFO, MONDO, DOID, and other controlled vocabularies (described in detail in this blog post). Using SeMRA's pre-constructed disease and phenotype prioritization mapping, these references can be standardized in a deterministic and principled way.

```python import chembldownloader import semra from semra.api import prioritizedf

A dataframe of indication-disease pairs, where the

"efo_id" column is actually an arbitrary disease or phenotype query

df = chembldownloader.query("SELECT DISTINCT drugindid, efoid FROM DRUGINDICATION")

a pre-calculated prioritization of diseases and phenotypes from MONDO, DOID,

HPO, ICD, GARD, and more.

url = "https://zenodo.org/records/15164180/files/priority.sssom.tsv?download=1" mappings = semra.from_sssom(url)

the dataframe will now have a new column with standardized references

prioritizedf(mappings, df, column="efoid", targetcolumn="priorityindication_curie") ```

🚀 Installation

The most recent release can be installed from PyPI with uv:

console $ uv pip install semra

or with pip:

console $ python3 -m pip install semra

The most recent code and data can be installed directly from GitHub with uv:

console $ uv pip install git+https://github.com/biopragmatics/semra.git

or with pip:

console $ python3 -m pip install git+https://github.com/biopragmatics/semra.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

📖 Citation

Assembly and reasoning over semantic mappings at scale for biomedical data integration
Hoyt, C. T., Karis K., and Gyori, B. M.
bioRxiv, 2025.04.16.649126

bibtex @article {hoyt2025semra, author = {Hoyt, Charles Tapley and Karis, Klas and Gyori, Benjamin M}, title = {Assembly and reasoning over semantic mappings at scale for biomedical data integration}, year = {2025}, doi = {10.1101/2025.04.16.649126}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2025/04/21/2025.04.16.649126}, journal = {bioRxiv} }

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution. ### Development Installation To install in development mode, use the following: ```console $ git clone git+https://github.com/biopragmatics/semra.git $ cd semra $ uv pip install -e . ``` Alternatively, install using pip: ```console $ python3 -m pip install -e . ``` ### Updating Package Boilerplate This project uses `cruft` to keep boilerplate (i.e., configuration, contribution guidelines, documentation configuration) up-to-date with the upstream cookiecutter package. Install cruft with either `uv tool install cruft` or `python3 -m pip install cruft` then run: ```console $ cruft update ``` More info on Cruft's update command is available [here](https://github.com/cruft/cruft?tab=readme-ov-file#updating-a-project). ### 🥼 Testing After cloning the repository and installing `tox` with `uv tool install tox --with tox-uv` or `python3 -m pip install tox tox-uv`, the unit tests in the `tests/` folder can be run reproducibly with: ```console $ tox -e py ``` Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/biopragmatics/semra/actions?query=workflow%3ATests). ### 📖 Building the Documentation The documentation can be built locally using the following: ```console $ git clone git+https://github.com/biopragmatics/semra.git $ cd semra $ tox -e docs $ open docs/build/html/index.html ``` The documentation automatically installs the package as well as the `docs` extra specified in the [`pyproject.toml`](pyproject.toml). `sphinx` plugins like `texext` can be added there. Additionally, they need to be added to the `extensions` list in [`docs/source/conf.py`](docs/source/conf.py). The documentation can be deployed to [ReadTheDocs](https://readthedocs.io) using [this guide](https://docs.readthedocs.io/en/stable/intro/import-guide.html). The [`.readthedocs.yml`](.readthedocs.yml) YAML file contains all the configuration you'll need. You can also set up continuous integration on GitHub to check not only that Sphinx can build the documentation in an isolated environment (i.e., with `tox -e docs-test`) but also that [ReadTheDocs can build it too](https://docs.readthedocs.io/en/stable/pull-requests.html). #### Configuring ReadTheDocs 1. Log in to ReadTheDocs with your GitHub account to install the integration at https://readthedocs.org/accounts/login/?next=/dashboard/ 2. Import your project by navigating to https://readthedocs.org/dashboard/import then clicking the plus icon next to your repository 3. You can rename the repository on the next screen using a more stylized name (i.e., with spaces and capital letters) 4. Click next, and you're good to go! ### 📦 Making a Release #### Configuring Zenodo [Zenodo](https://zenodo.org) is a long-term archival system that assigns a DOI to each release of your package. 1. Log in to Zenodo via GitHub with this link: https://zenodo.org/oauth/login/github/?next=%2F. This brings you to a page that lists all of your organizations and asks you to approve installing the Zenodo app on GitHub. Click "grant" next to any organizations you want to enable the integration for, then click the big green "approve" button. This step only needs to be done once. 2. Navigate to https://zenodo.org/account/settings/github/, which lists all of your GitHub repositories (both in your username and any organizations you enabled). Click the on/off toggle for any relevant repositories. When you make a new repository, you'll have to come back to this After these steps, you're ready to go! After you make "release" on GitHub (steps for this are below), you can navigate to https://zenodo.org/account/settings/github/repository/biopragmatics/semra to see the DOI for the release and link to the Zenodo record for it. #### Registering with the Python Package Index (PyPI) You only have to do the following steps once. 1. Register for an account on the [Python Package Index (PyPI)](https://pypi.org/account/register) 2. Navigate to https://pypi.org/manage/account and make sure you have verified your email address. A verification email might not have been sent by default, so you might have to click the "options" dropdown next to your address to get to the "re-send verification email" button 3. 2-Factor authentication is required for PyPI since the end of 2023 (see this [blog post from PyPI](https://blog.pypi.org/posts/2023-05-25-securing-pypi-with-2fa/)). This means you have to first issue account recovery codes, then set up 2-factor authentication 4. Issue an API token from https://pypi.org/manage/account/token #### Configuring your machine's connection to PyPI You have to do the following steps once per machine. ```console $ uv tool install keyring $ keyring set https://upload.pypi.org/legacy/ __token__ $ keyring set https://test.pypi.org/legacy/ __token__ ``` Note that this deprecates previous workflows using `.pypirc`. #### Uploading to PyPI After installing the package in development mode and installing `tox` with `uv tool install tox --with tox-uv` or `python3 -m pip install tox tox-uv`, run the following from the console: ```console $ tox -e finish ``` This script does the following: 1. Uses [bump-my-version](https://github.com/callowayproject/bump-my-version) to switch the version number in the `pyproject.toml`, `CITATION.cff`, `src/semra/version.py`, and [`docs/source/conf.py`](docs/source/conf.py) to not have the `-dev` suffix 2. Packages the code in both a tar archive and a wheel using [`uv build`](https://docs.astral.sh/uv/guides/publish/#building-your-package) 3. Uploads to PyPI using [`uv publish`](https://docs.astral.sh/uv/guides/publish/#publishing-your-package). 4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped. 5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use `tox -e bumpversion -- minor` after. #### Releasing on GitHub 1. Navigate to https://github.com/biopragmatics/semra/releases/new to draft a new release 2. Click the "Choose a Tag" dropdown and select the tag corresponding to the release you just made 3. Click the "Generate Release Notes" button to get a quick outline of recent changes. Modify the title and description as you see fit 4. Click the big green "Publish Release" button This will trigger Zenodo to assign a DOI to your release as well.